Created by: Exception-star

• 版本、环境信息：    1）PaddlePaddle版本：1.5.2    2）GPU：4 个k40m    4）系统环境：linux、Python3.7 cuda：8.0 cudnn：7.2

模型名称 ：DANet(Dual Attention Network for Scene Segmentation） 使用数据集名称 ：Cityscapes 模型链接 📎 https://arxiv.org/abs/1809.02983

• 训练信息

• 问题描述：加了strategy.sync_batch_norm=True报错。不加不会报错

代码片段：

def main(args):
    image_shape = args.crop_size
    image = fluid.layers.data(name='image', shape=[3, image_shape, image_shape], dtype='float32')
    label = fluid.layers.data(name='label', shape=[image_shape, image_shape], dtype='int64')

    batch_size = args.batch_size
    epoch_num = args.epoch_num
    num_classes = args.num_classes
    data_root = args.data_folder

    # program
    start_prog = fluid.default_startup_program()
    train_prog = fluid.default_main_program()

    # clone 必须在优化器之前
    test_prog = train_prog.clone(for_test=True)

    logging.basicConfig(level=logging.INFO, filename='{}/DANet_train_parallel_executor.log'.format(args.log_root),
                        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    logging.info('DANet')
    logging.info(args)

    with fluid.program_guard(train_prog, start_prog):
        with fluid.unique_name.guard():
            # train_py_reader 
            train_py_reader = fluid.io.PyReader(feed_list=[image, label],
                                                capacity=4,
                                                use_double_buffer=True,
                                                iterable=False)
            train_data = cityscapes_train(data_root=data_root,
                                          base_size=args.base_size,
                                          crop_size=args.crop_size,
                                          scale=args.scale,
                                          xmap=False)
            batch_train_data = paddle.batch(paddle.reader.shuffle(
                train_data, buf_size=batch_size * 3),
                batch_size=batch_size,
                drop_last=True)
            train_py_reader.decorate_sample_list_generator(batch_train_data)

            model = get_model(args)
            pred, pred2, pred3 = model(image)
            # print(pred.shape)
            train_loss = loss_fn(pred, pred2, pred3, label)
            train_avg_loss = fluid.layers.mean(train_loss)
            optimizer = optimizer_setting(args)
            optimizer.minimize(train_avg_loss)

            miou, wrong, correct = mean_iou(pred, label, num_classes=num_classes)

    with fluid.program_guard(test_prog, start_prog):
        with fluid.unique_name.guard():
            test_py_reader = fluid.io.PyReader(feed_list=[image, label],
                                               capacity=4,
                                               iterable=False,
                                               use_double_buffer=True)
            val_data = cityscapes_val(data_root=data_root,
                                      base_size=args.base_size,
                                      crop_size=args.crop_size,
                                      scale=args.scale,
                                      xmap=False)
            batch_test_data = paddle.batch(val_data,
                                           batch_size=batch_size,
                                           drop_last=True)
            test_py_reader.decorate_sample_list_generator(batch_test_data)

            model = get_model(args)
            pred, pred2, pred3 = model(image)

            test_loss = loss_fn(pred, pred2, pred3, label)
            test_avg_loss = fluid.layers.mean(test_loss)
            miou, wrong, correct = mean_iou(pred, label, num_classes=19)

    place = fluid.CUDAPlace(0) if args.cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(start_prog)

    exec_strategy = fluid.ExecutionStrategy()
    exec_strategy.num_threads = fluid.core.get_cuda_device_count()
    # exec_strategy.num_iteration_per_drop_scope = 100
    build_strategy = fluid.BuildStrategy()
    build_strategy.sync_batch_norm = True
    build_strategy.enable_inplace = True

    if args.use_data_parallel:
        compiled_train_prog = fluid.compiler.CompiledProgram(train_prog).with_data_parallel(
            loss_name=train_avg_loss.name,
            build_strategy=build_strategy,
            exec_strategy=exec_strategy)
    else:
        compiled_train_prog = train_prog


    # 加载模型
    save_dir = 'checkpoint_parallel_executor/DAnet_better_train_0.1829'
    if os.path.exists(save_dir):
        load_model(save_dir, exe, program=train_prog)

    train_iou_manager = fluid.metrics.Accuracy()
    train_avg_loss_manager = fluid.metrics.Accuracy()
    test_iou_manager = fluid.metrics.Accuracy()
    test_avg_loss_manager = fluid.metrics.Accuracy()
    better_miou_train = 0
    better_miou_test = 0

    # train_loss_title = 'Train_loss'
    # test_loss_title = 'Test_loss'
    #
    # train_iou_title = 'Train_mIOU'
    # test_iou_title = 'Test_mIOU'

    # plot_loss = Ploter(train_loss_title, test_loss_title)
    # plot_iou = Ploter(train_iou_title, test_iou_title)

    for epoch in range(epoch_num):
        prev_time = datetime.now()
        train_avg_loss_manager.reset()
        train_iou_manager.reset()
        logging.info('training, epoch = {}'.format(epoch + 1))
        train_py_reader.start()
        batch_id = 0
        while True:
            try:
                train_fetch_list = [train_avg_loss, miou, wrong, correct]
                train_avg_loss_value, train_iou_value, _, _ = exe.run(program=compiled_train_prog,
                                                                      fetch_list=train_fetch_list)
                # print(pred_s.shape)
                train_iou_manager.update(train_iou_value, weight=batch_size)
                train_avg_loss_manager.update(train_avg_loss_value, weight=batch_size)
                batch_train_str = "epoch: {}, batch: {}, train_avg_loss: {:.6f}, " \
                                  "train_miou: {:.6f}.".format(epoch + 1,
                                                               batch_id + 1,
                                                               train_avg_loss_value[0],
                                                               train_iou_value[0])
                batch_id += 1
                # if batch_id % 30 == 0:
                logging.info(batch_train_str)
                print(batch_train_str)
            except fluid.core.EOFException:
                train_py_reader.reset()
                break
        cur_time = datetime.now()
        h, remainder = divmod((cur_time - prev_time).seconds, 3600)
        m, s = divmod(remainder, 60)
        time_str = " Time %02d:%02d:%02d" % (h, m, s)
        train_str = "epoch: {}, train_avg_loss: {:.6f}, " \
                    "train_miou: {:.6f}.".format(epoch + 1,
                                                 train_avg_loss_manager.eval()[0],
                                                 train_iou_manager.eval()[0])
        print(train_str + time_str + '\n')
        logging.info(train_str + time_str)
        # plot_loss.append(train_loss_title, epoch, train_avg_loss_manager.eval()[0])
        # plot_loss.plot('./DANet_loss.jpg')
        # plot_iou.append(train_iou_title, epoch, train_iou_manager.eval()[0])
        # plot_iou.plot('./DANet_miou.jpg')

        # save_model
        if better_miou_train < train_iou_manager.eval()[0]:
            shutil.rmtree('./checkpoint_parallel_executor/DAnet_better_train_{:.4f}'.format(better_miou_train),
                          ignore_errors=True)
            better_miou_train = train_iou_manager.eval()[0]
            logging.warning('better_train: {:.6f}, epoch: {}, successful save train model!\n'.format(better_miou_train, epoch + 1))
            save_dir = './checkpoint_parallel_executor/DAnet_better_train_{:.4f}'.format(better_miou_train)
            save_model(save_dir, exe, program=train_prog)